Improvement initial solution water flow like algorithm using simulated annealing for travelling salesman problem

Anis Aklima Kamarudin, Zulaiha Ali Othman, Hafiz Mohd Sarim

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The water flow like algorithm (WFA) is a relatively new metaheuristic algorithm, which has shown good solution for the travelling salesman problem (TSP) and is comparable to state of the art results. There are various factor influence the performance of WFA for TSP. However, initial solution has also influence the performance of the algorithm. The basic of WFA uses a random searching method for initialization technique. Previous WFA-TSP used nearest neighbor for initial solution. Therefore this paper presents the performance of use simulated annealing (SA) in initial solution for WFA-TSP. The algorithms are evaluated using 16 benchmarks TSP datasets. The experimental results show that the proposed SA-WFA-TSP outperforms due to its capacity of reduce computing time compared with others algorithms especially for large dataset. Therefore, it can be concluded that SA-WFA-TSP has become the state of the art algorithm for TSP.

Original languageEnglish
Pages (from-to)63-66
Number of pages4
JournalInternational Review of Management and Marketing
Volume6
Issue number8Special Issue
Publication statusPublished - 2016

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Water
Traveling salesman problem
Simulated annealing algorithm
Simulated annealing
Nearest neighbor
Influence factors
Benchmark
Metaheuristics

Keywords

  • Combinatorial optimization
  • Nature-inspired metaheuristics
  • Simulated annealing algorithm
  • Traveling salesman problem
  • Water flow liked algorithm

ASJC Scopus subject areas

  • Business, Management and Accounting(all)

Cite this

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title = "Improvement initial solution water flow like algorithm using simulated annealing for travelling salesman problem",
abstract = "The water flow like algorithm (WFA) is a relatively new metaheuristic algorithm, which has shown good solution for the travelling salesman problem (TSP) and is comparable to state of the art results. There are various factor influence the performance of WFA for TSP. However, initial solution has also influence the performance of the algorithm. The basic of WFA uses a random searching method for initialization technique. Previous WFA-TSP used nearest neighbor for initial solution. Therefore this paper presents the performance of use simulated annealing (SA) in initial solution for WFA-TSP. The algorithms are evaluated using 16 benchmarks TSP datasets. The experimental results show that the proposed SA-WFA-TSP outperforms due to its capacity of reduce computing time compared with others algorithms especially for large dataset. Therefore, it can be concluded that SA-WFA-TSP has become the state of the art algorithm for TSP.",
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AU - Kamarudin, Anis Aklima

AU - Ali Othman, Zulaiha

AU - Mohd Sarim, Hafiz

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N2 - The water flow like algorithm (WFA) is a relatively new metaheuristic algorithm, which has shown good solution for the travelling salesman problem (TSP) and is comparable to state of the art results. There are various factor influence the performance of WFA for TSP. However, initial solution has also influence the performance of the algorithm. The basic of WFA uses a random searching method for initialization technique. Previous WFA-TSP used nearest neighbor for initial solution. Therefore this paper presents the performance of use simulated annealing (SA) in initial solution for WFA-TSP. The algorithms are evaluated using 16 benchmarks TSP datasets. The experimental results show that the proposed SA-WFA-TSP outperforms due to its capacity of reduce computing time compared with others algorithms especially for large dataset. Therefore, it can be concluded that SA-WFA-TSP has become the state of the art algorithm for TSP.

AB - The water flow like algorithm (WFA) is a relatively new metaheuristic algorithm, which has shown good solution for the travelling salesman problem (TSP) and is comparable to state of the art results. There are various factor influence the performance of WFA for TSP. However, initial solution has also influence the performance of the algorithm. The basic of WFA uses a random searching method for initialization technique. Previous WFA-TSP used nearest neighbor for initial solution. Therefore this paper presents the performance of use simulated annealing (SA) in initial solution for WFA-TSP. The algorithms are evaluated using 16 benchmarks TSP datasets. The experimental results show that the proposed SA-WFA-TSP outperforms due to its capacity of reduce computing time compared with others algorithms especially for large dataset. Therefore, it can be concluded that SA-WFA-TSP has become the state of the art algorithm for TSP.

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